The normal distribution may be a form presenting data by arranging the probability distribution of every value within the data. Most values remain round the mean making the arrangement symmetric.

We use various functions in numpy library to mathematically calculate the values for a traditional distribution. Histograms are created over which we plot the probability distribution curve.

import matplotlib.pyplot as plt
import numpy as np
mu, sigma = 0.5, 0.1
s = np.random.normal(mu, sigma, 1000)
# Create the bins and histogram
count, bins, ignored = plt.hist(s, 20, normed=True)
# Plot the distribution curve
plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
 np.exp( - (bins - mu)**2 / (2 * sigma**2) ), linewidth=3, color='y')
plt.show()

Its output is as follows −

12.1 1

Python – Bernoulli distribution

The Bernoulli distribution model deals with finding the probability of success of an occasion which has only two possible outcomes during a series of experiments. for instance, tossing of a coin always gives a head or a tail. The probability of finding exactly 3 heads in tossing a coin repeatedly for 10 times is estimated during the Bernoulli distribution.

We use the seaborn python library which has in-built functions to make such probability distribution graphs. Also, the scipy package helps is creating the Bernoulli distribution.

from scipy.stats import binom
import seaborn as sb
binom.rvs(size=10,n=20,p=0.8)
data_binom = binom.rvs(n=20,p=0.8,loc=0,size=1000)
ax = sb.distplot(data_binom,
kde=True,
color='blue',
hist_kws={"linewidth": 25,'alpha':1})
ax.set(xlabel='Binomial', ylabel='Frequency')

Its output is as follows −

12.2

Python – Poisson distribution

A Poisson distribution may be a distribution which shows the likely number of your time s that an occasion will occur within a pre-determined period of time. it’s used for independent events which occur at a continuing rate within a given interval of your time. The Poisson distribution may be a discrete function, meaning that the event can only be measured as occurring or not as occurring, meaning the variable can only be measured in whole numbers.

We use the seaborn python library which has in-built functions to make such probability distribution graphs. Also, the scipy package helps is creating the Bernoulli distribution.

from scipy.stats import poisson
import seaborn as sb
data_binom = poisson.rvs(mu=4, size=10000)
ax = sb.distplot(data_binom,
kde=True,
color='green',
hist_kws={"linewidth": 25,'alpha':1})
ax.set(xlabel='Poisson', ylabel='Frequency')

Its output is as follows –

12.3

So, this brings us to the end of blog. This Tecklearn ‘Normal , Binomial and Poisson Distribution in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Data Science domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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